# Viewpoint Recommendation for Point Cloud Labeling through Interaction Cost Modeling

arXiv: https://arxiv.org/abs/2602.10871 · HTML: https://mlsanity.com/paper/2602.10871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 68 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation](https://mlsanity.com/paper/2003.14032/) (2020)
- [Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark](https://mlsanity.com/paper/1704.03847/) (2017)
- [3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams](https://mlsanity.com/paper/1905.00525/) (2019)
- [Interactive Object Segmentation in 3D Point Clouds](https://mlsanity.com/paper/2204.07183/) (2022)
- [LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking](https://mlsanity.com/paper/1904.09085/) (2019)
- [Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning](https://mlsanity.com/paper/2202.12588/) (2022)
- [LassoNet: Deep Lasso-Selection of 3D Point Clouds](https://mlsanity.com/paper/1907.13538/) (2019)
- [OneLabeler: A Flexible System for Building Data Labeling Tools](https://mlsanity.com/paper/2203.14227/) (2022)
- [Simulation-Based Optimization of User Interfaces for Quality-Assuring Machine Learning Model Predictions](https://mlsanity.com/paper/2104.01129/) (2021)

## Abstract (arXiv)

Semantic segmentation of 3D point clouds is important for many applications, such as autonomous driving. To train semantic segmentation models, labeled point cloud segmentation datasets are essential. Meanwhile, point cloud labeling is time-consuming for annotators, which typically involves tuning the camera viewpoint and selecting points by lasso. To reduce the time cost of point cloud labeling, we propose a viewpoint recommendation approach to reduce annotators' labeling time costs. We adapt Fitts' law to model the time cost of lasso selection in point clouds. Using the modeled time cost, the viewpoint that minimizes the lasso selection time cost is recommended to the annotator. We build a data labeling system for semantic segmentation of 3D point clouds that integrates our viewpoint recommendation approach. The system enables users to navigate to recommended viewpoints for efficient annotation. Through an ablation study, we observed that our approach effectively reduced the data labeling time cost. We also qualitatively compare our approach with previous viewpoint selection approaches on different datasets.